{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/tile2vec-unsupervised-representation-learning","title":"Tile2Vec: Unsupervised representation learning for spatially distributed data","arxiv_id":"1805.02855","date":"2018-05-08","proceeding":null,"authors":["Neal Jean","Sherrie Wang","Anshul Samar","George Azzari","David Lobell","Stefano Ermon"],"abstract":"Geospatial analysis lacks methods like the word vector representations and\npre-trained networks that significantly boost performance across a wide range\nof natural language and computer vision tasks. To fill this gap, we introduce\nTile2Vec, an unsupervised representation learning algorithm that extends the\ndistributional hypothesis from natural language -- words appearing in similar\ncontexts tend to have similar meanings -- to spatially distributed data. We\ndemonstrate empirically that Tile2Vec learns semantically meaningful\nrepresentations on three datasets. Our learned representations significantly\nimprove performance in downstream classification tasks and, similar to word\nvectors, visual analogies can be obtained via simple arithmetic in the latent\nspace.","url_abs":"http://arxiv.org/abs/1805.02855v2","url_pdf":"http://arxiv.org/pdf/1805.02855v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"tile2vec-unsupervised-representation-learning","repo_url":"https://github.com/ermongroup/tile2vec","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null},{"paper_slug":"tile2vec-unsupervised-representation-learning","repo_url":"https://github.com/acmiyaguchi/birdclef-2022","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"tile2vec-unsupervised-representation-learning","repo_url":"https://github.com/jiankang1991/SauMoCo","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}},{"paper_slug":"tile2vec-unsupervised-representation-learning","repo_url":"https://github.com/simongrest/farm-pin-crop-detection-challenge","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"Apache-2.0"}}],"tasks":[{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"representation-learning","task_name":"Representation Learning"},{"task_slug":"visual-analogies","task_name":"Visual Analogies"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1805.02855","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1805.02855"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/ermongroup/tile2vec","reach":null},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/jiankang1991/SauMoCo","reach":{"status":"ok"}},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/simongrest/farm-pin-crop-detection-challenge","reach":{"status":"ok","spdx":"Apache-2.0"}},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/acmiyaguchi/birdclef-2022","reach":null}],"summary":{"ran_draft_wrong":1},"by_repo_kind":{"official":{"samples":1,"ran":1,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":0,"samples":[{"code_sha256_prefix":"a7bf16ad939a6c7b","entry":"make_tilenet","repo":"ermongroup/tile2vec","repo_kind":"official","path":"src/tilenet.py","file_url":"https://github.com/ermongroup/tile2vec/blob/HEAD/src/tilenet.py","link_basis":"first_harvest_node","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"a7bf16ad939a6c7b"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}